Day 2

6/27/2025

13:00 - 14:00

AI-Co-Taught Classrooms

With the rapid advancements of AI technologies, technological readiness, ethical considerations, and pedagogical innovation are all essential components of research that aims to integrate them into classroom instruction. This presentation proposes a practical and ethically justified framework for AI-co-taught classrooms, with AI as a dynamic assistant that supplements (not substitutes) human teachers. In this paper, we suggest a pragmatic and ethical framework for AI-co-taught classrooms, in which the AI functions as a dynamic assistant to instructors rather than a substitute. The framework elucidates how AI tools can offload cognitive load, allowing teachers to focus on higher-level facilitation while AI takes on more routine administrative and feedback tasks. It is based on the theories of cognitive load (Sweller, 1988), distributed cognition (Hollan et al., 2000), and Vygotsky’s ZPD. The framework supports the recently developed ethical AI guidelines of the European Commission (2022) and the GenAI Higher Education Consortium (2024) that promote transparency, documented privacy for students, and non-biased, informed consent.

The paper reflects on the potential benefits of AI support, enhanced quality of feedback, multilingual scaffolding, time saving, and learner autonomy, as well as on the challenges that it brings about, such as overdependence, algorithmic bias, and deskilling of the teacher. Specific classroom examples of EFL programs within Turkish and international universities illustrate how AI can generate vocabulary practice, examine grammar, summarize texts, and even co-facilitate Socratic seminars. In this session, participants leave with a practical framework for planning, sample co-teaching task maps, and an ethical integration checklist. The study has observed the learning effect, teachers’ workload, and students’ feedback, which suggests future investigation. Finally, this model promotes AI in higher education to be fair, scalable, and human-centered. Furthermore, in this sense, the latter approach pushes AI for higher education to be accessible, scalable, and human-oriented.